Due to ever increasing video resolutions, and rising expectations for high quality video images, a high demand exists for efficient image data compression of video while performance is limited for coding with existing video coding standards such as VP9, Alliance Open Media Version 1 (AV1), H.264, H.265/HEVC (High Efficiency Video Coding) standard, and so forth. The aforementioned standards use expanded forms of traditional approaches to address the insufficient compression/quality problem, but often the results are still insufficient.
Conventional video codec encodes or decodes frames of a video sequence where each frame is formed of pixel data, and the codec performs a number of operations including both lossy and lossless compression and decompression. Relevant here, the lossless compression and decompression may include entropy coding that replaces a large number of bits of one or more symbols with one bit or at least a much smaller number of bits. In arithmetic entropy coding methods, a probability context is determined to compute the probabilities that a bit will be a certain symbol among a set of possible symbols (such as all alpha-numeric symbols). These probabilities are then used to determine a binary or other value to represent one or more of the bits. The probability context for a current pixel location may be determined, at least in part, by the data of the spacial neighbor pixels adjacent to the current pixel location. This results in the decoding of the current location to be delayed until the data of those spacial neighbors are decoded, which is usually performed row by row down a frame (or picture) in raster order. This causes significant delay and lower throughput in the form of a lower bitrate.